updating Fit1D class

src/tavi/data/fit.py

@@ -83,7 +83,7 @@ def __init__(
 
         self.background_models: models = []
         self.signal_models: models = []
-        self.parameters: Optional[Parameters] = None
+        self._parameters: Optional[Parameters] = None
         self._num_backgrounds = 0
         self._num_signals = 0
         self.result: Optional[ModelResult] = None
@@ -122,13 +122,7 @@ def add_signal(
     ):
         self._num_signals += 1
         prefix = f"s{self._num_signals}_"
-        self.signal_models.append(
-            Fit1D._add_model(
-                model,
-                prefix,
-                nan_policy=self.nan_policy,
-            )
-        )
+        self.signal_models.append(Fit1D._add_model(model, prefix, nan_policy=self.nan_policy))
 
     def add_background(
         self,
@@ -143,13 +137,7 @@ def add_background(
     ):
         self._num_backgrounds += 1
         prefix = f"b{self._num_backgrounds}_"
-        self.background_models.append(
-            Fit1D._add_model(
-                model,
-                prefix,
-                nan_policy=self.nan_policy,
-            )
-        )
+        self.background_models.append(Fit1D._add_model(model, prefix, nan_policy=self.nan_policy))
 
     @staticmethod
     def _get_param_names(models) -> list[list[str]]:
@@ -166,11 +154,15 @@ def signal_param_names(self):
     def background_param_names(self):
         return Fit1D._get_param_names(self.background_models)
 
-    @staticmethod
-    def _get_params(all_pars, parsms_names: list[list[str]]) -> tuple[tuple[FitParams, ...], ...]:
-        signal_params_list = []
+    @property
+    def params(self) -> dict[str, tuple[FitParams, ...]]:
+
+        all_pars = self.guess() if self._parameters is None else self._parameters
+        parsms_names = Fit1D._get_param_names(self.signal_models + self.background_models)
+        params_dict = {}
 
         for params in parsms_names:
+            key = params[0].split("_")[0]
             params_list = []
             for param_name in params:
                 param = all_pars[param_name]
@@ -185,34 +177,17 @@ def _get_params(all_pars, parsms_names: list[list[str]]) -> tuple[tuple[FitParam
                         brute_step=param.brute_step,
                     )
                 )
-            signal_params_list.append(tuple(params_list))
-        return tuple(signal_params_list)
-
-    @property
-    def signal_params(self) -> tuple[tuple[FitParams, ...], ...]:
-
-        pars = self.guess() if self.parameters is None else self.parameters
-        names = Fit1D._get_param_names(self.signal_models)
-        signal_params = Fit1D._get_params(pars, names)
-
-        return signal_params
-
-    @property
-    def background_params(self) -> tuple[tuple[FitParams, ...], ...]:
-
-        pars = self.guess() if self.parameters is None else self.parameters
-        names = Fit1D._get_param_names(self.background_models)
-        background_params = Fit1D._get_params(pars, names)
+            params_dict.update({key: tuple(params_list)})
 
-        return background_params
+        return params_dict
 
     def guess(self) -> Parameters:
         pars = Parameters()
         for signal in self.signal_models:
             pars += signal.guess(self.y, x=self.x)
         for bkg in self.background_models:
             pars += bkg.guess(self.y, x=self.x)
-        self.parameters = pars
+        self._parameters = pars
         return pars
 
     @property

tests/test_fit.py

@@ -58,16 +58,17 @@ def test_get_fitting_variables(fit_data):
     assert f1.signal_param_names == [["s1_amplitude", "s1_center", "s1_sigma"]]
     assert f1.background_param_names == [["b1_c"]]
 
-    assert len(f1.signal_params) == 1
-    assert len(f1.signal_params[0]) == 5
+    assert len(f1.params) == 2
 
-    assert f1.signal_params[0][0].name == "s1_amplitude"
-    assert f1.signal_params[0][1].name == "s1_center"
-    assert f1.signal_params[0][2].name == "s1_sigma"
-    assert f1.signal_params[0][3].name == "s1_fwhm"
-    assert f1.signal_params[0][4].name == "s1_height"
-    assert f1.signal_params[0][4].expr == "0.3989423*s1_amplitude/max(1e-15, s1_sigma)"
-    assert f1.background_params[0][0].name == "b1_c"
+    s1_params = f1.params["s1"]
+    assert len(s1_params) == 5
+    assert s1_params[0].name == "s1_amplitude"
+    assert s1_params[1].name == "s1_center"
+    assert s1_params[2].name == "s1_sigma"
+    assert s1_params[3].name == "s1_fwhm"
+    assert s1_params[4].name == "s1_height"
+    assert s1_params[4].expr == "0.3989423*s1_amplitude/max(1e-15, s1_sigma)"
+    assert f1.params["b1"][0].name == "b1_c"
 
 
 def test_guess_initial(fit_data):
@@ -117,9 +118,9 @@ def test_fit_two_peak(fit_data):
 
     s1_scan, PLOT = fit_data
 
-    f1 = Fit1D(s1_scan, fit_range=(0.0, 4.0))
+    f1 = Fit1D(s1_scan, fit_range=(0.0, 4.0), name="scan42_fit2peaks")
 
-    f1.add_background(values=(0.7,))
+    f1.add_background(model="Constant")
     f1.add_signal(values=(None, 3.5, 0.29), vary=(True, True, True))
     f1.add_signal(
         model="Gaussian",